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Businesses manage data to understand the connections between their customers, products or services, features, markets, and anything else that affects the business. With a knowledge graph, you can represent these connections directly to analyze and understand the compound relationships that drive business innovation. This report introduces knowledge graphs and examines their ability to weave business data and business knowledge into an architecture known as a data fabric .

Authors Sean Martin, Ben Szekely, and Dean Allemang explain graph data and knowledge representation and demonstrate the value of combining these two things in a knowledge graph. You'll learn how knowledge graphs enable an enterprise-scale data fabric and discover what to expect in the near future as this technology evolves. This report also examines the evolution of databases, data integration, and data analysis to help you understand how the industry reached this point.

  • Learn how graph technology enables you to represent knowledge and link it to data
  • Understand how graph technology emphasizes the connected nature of data
  • Use a data fabric to support other data-intensive tasks, including machine learning and data analysis
  • Examine how a data fabric supports intense data-driven business initiatives more robustly than a simple database or data architecture

Table of Contents

  1. The Rise of the Knowledge Graph
    1. Executive Summary
    2. Introduction
    3. Emergence of the Knowledge Graph
    4. Semantic Systems
    5. Data Representation
    6. Bringing It Together: The Need for the Knowledge Graph
    7. Data and Graphs: A Quick Introduction
    8. Why Graphs Are Cool
    9. Graph Data Use Cases
    10. Advantages of Graph Standardization
    11. Technology Independence
    12. Publishing and Subscribing to Graph Data
    13. Standardizing Graphs: The Resource Description Framework
    14. Future-Proofing Your Data
    15. Querying Graphs: SPARQL
    16. The Knowledge Layer
    17. What Is a Vocabulary?
    18. Managing Vocabularies in an Enterprise
    19. What Is an Ontology?
    20. Ontology Versus Data Model
    21. Ontology and Representation
    22. Why Knowledge Is Cool
    23. Knowledge Use Cases
    24. Standardizing Knowledge Representation
    25. Standardizing Vocabulary: SKOS
    26. Standardizing Concepts: RDFS
    27. Representing Data and Knowledge in Graphs: The Knowledge Graph
    28. Classes and Instances
    29. Digging Deeper into the Knowledge Graph
    30. Why Knowledge Graphs Are Cool 
    31. Knowledge Graph Use Cases
    32. The Network Effect of the Knowledge Graph 
    33. Beyond Knowledge Graphs: An Integrated Data Enterprise
    34. Data Architecture Failure Modes
    35. Knowledge Graph Technology for a Data Fabric
    36. Getting Started on Building Your Data Fabric
    37. Motivation for a Data Fabric
    38. Timing of the Data Fabric
    39. References
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